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Found 7 papers, 0 papers with code
Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
We demonstrate improvements in TB detection performance ($\sim$12. 7% and $\sim$13. 4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i. e., non pre-trained) ViT models, achieving top performances of 0. 959 AUC and 0. 962 AUPR in adult TB detection, and 0. 697 AUC and 0. 607 AUPR in zero-shot pediatric TB detection.
Quantitative Metrics for Benchmarking Medical Image Harmonization
However, benchmarking the effectiveness of harmonization techniques has been a challenge due to the lack of widely available standardized datasets with ground truths.
Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI
To prevent hallucination in medical imaging, such as magnetic resonance images (MRI) of the brain, we propose a one-shot learning method where we utilize neural style transfer for harmonization.
SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model
However, the traditional diagnostic tool design methods based on supervised learning are burdened by the need to provide training data annotation, which should be of good quality for better clinical outcomes.
Ranked #1 on
Semantic Segmentation
on Montgomery County X-ray Set
GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images
Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically generated images that can be useful in training other models that require a large number of labelled or unlabelled data.
Train, Learn, Expand, Repeat
High-quality labeled data is essential to successfully train supervised machine learning models.
Learn to Segment Organs with a Few Bounding Boxes
Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology.
